WO2012109383A2 - Procédé de détermination de caractéristiques de liaison d'un médicament candidat à une protéine - Google Patents

Procédé de détermination de caractéristiques de liaison d'un médicament candidat à une protéine Download PDF

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Publication number
WO2012109383A2
WO2012109383A2 PCT/US2012/024369 US2012024369W WO2012109383A2 WO 2012109383 A2 WO2012109383 A2 WO 2012109383A2 US 2012024369 W US2012024369 W US 2012024369W WO 2012109383 A2 WO2012109383 A2 WO 2012109383A2
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Prior art keywords
binding
drug
drug candidate
dsc
hsa
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PCT/US2012/024369
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English (en)
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WO2012109383A3 (fr
Inventor
Jonathan B. Chaires
Nichola C. Garbett
Albert S. Benight
Daniel J. Fish
Greg P. BREWOOD
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University Of Louisville Research Foundation, Inc.
Louisville Bioscience, Inc.
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Publication of WO2012109383A2 publication Critical patent/WO2012109383A2/fr
Publication of WO2012109383A3 publication Critical patent/WO2012109383A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/557—Immunoassay; Biospecific binding assay; Materials therefor using kinetic measurement, i.e. time rate of progress of an antigen-antibody interaction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/20—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity
    • G01N25/48—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity on solution, sorption, or a chemical reaction not involving combustion or catalytic oxidation
    • G01N25/4846—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity on solution, sorption, or a chemical reaction not involving combustion or catalytic oxidation for a motionless, e.g. solid sample
    • G01N25/4866—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity on solution, sorption, or a chemical reaction not involving combustion or catalytic oxidation for a motionless, e.g. solid sample by using a differential method
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/15—Medicinal preparations ; Physical properties thereof, e.g. dissolubility

Definitions

  • Embodiments herein relate to the field of drug discovery, and, more specifically, to systems and methods for predicting drug efficacy using Differential Scanning Calorimetry.
  • ADMET absorption, distribution, metabolism, excretion, and toxicity
  • Figures 1A and 1 B show the results of Differential Scanning Calorimetry (DSC) and fluorescence melting curves of Human Serum Albumin (HSA) in phosphate buffer ( Figure 1A) and phosphate buffer with 5% DM50 ( Figure 1 B), in accordance with various
  • Figure 2 shows example measurements of ligand binding (bromocresol green) to HSA by DSC, fluorescence melting, and Isothermal Titration Calorimetry (ITC), in
  • Figure 3 shows example measurements of ligand binding (naproxen green) to
  • Figure 4 shows example measurements of ligand binding (salicylate) to HSA by
  • Figure 5 shows example measurements of ligand binding (bromophenol blue) to
  • Figures 6A and 6B show example measurements of ligand binding (phenol red) to HSA by DSC, fluorescence melting, and ITC, in accordance with various embodiments;
  • Figure 7 shows example measurements of ligand binding (bromosulfalein) to
  • Figure 8 shows example measurements of ligand binding (ibuprofen) to HSA by
  • Figure 9 shows example measurements of ligand binding (imipramine) to HSA by DSC, fluorescence melting, and ITC, in accordance with various embodiments;
  • FigurelO shows example measurements of ligand binding (chlorpromazine) to
  • Figure 11 shows example measurements of ligand binding (oxacillin) to HSA by
  • Figure 12 shows example measurements of ligand binding (penicillin G) to HSA by DSC, fluorescence melting, and ITC, in accordance with various embodiments;
  • Figure 13 shows example measurements of ligand binding (Evan's blue) to HSA by DSC, fluorescence melting, and ITC, in accordance with various embodiments;
  • Figure 14 shows example measurements of ligand binding (methyl orange) to
  • Figure15 shows example measurements of ligand binding (oetanoate) to HSA by DSC, fluorescence melting, and ITC, in accordance with various embodiments;
  • Figure 16 shows example measurements of ligand binding (sodium oleate) to
  • Figure 17 shows a data summary table of DSC and fluorescence melting of HSA in the presence of 18 different ligands, wherein the explicit data as shown in Figures 2-16 is not shown, in accordance with various embodiments;
  • Figure 18 shows a data summary table of DSC and fluorescence melting with
  • HSA-ligand (1 :10) in potassium phosphate buffer + DM50 in the presence of an additional 15 different ligands, wherein the explicit experimental data as shown in Figures 2-16 is not shown, in accordance with various embodiments;
  • Figure 19 shows a table having DSC of HSA melting in the presence of different binding ligands, wherein the table summarizes results of DSC melting of HSA in the presence of different binding ligands grouped according to binding site and observed ligand-dependent changes, in accordance with various embodiments;
  • Figure 20 shows a summary table of ITC saturation binding data, wherein the fitting parameters obtained from ITC saturation binding for the ligands examined are
  • Figure 21 shows a table of ITC excess site data, wherein ITC excess binding is summarized, and most cases show two binding sites, in accordance with various embodiments;
  • FIG. 22 illustrates complementarities of Surface Plasmon Resonance (SPR) and DSC, in accordance with various embodiments;
  • Figure 23 illustrates interactions with individual proteins, in accordance with various embodiments.
  • Figure 24 illustrates a strategy to screen a library for HSA binding, in
  • Figure 25 illustrates a screen for binding to plasma proteins, in accordance with various embodiments
  • Figure 26 illustrates drug interactions in situ, in accordance with various embodiments
  • Figure 27 shows DSC thermograms of HSA in the presence of increasing amounts of penicillin, in accordance with various embodiments
  • Figure 28 shows DSC thermograms of HSA in the presence of increasing amounts of bilirubin, in accordance with various embodiments
  • Figure 29 shows calculations of Kn » Kd, wherein the simple equilibrium binding model indicates the observed behavior for penicillin is consistent with single-site binding to the native state of HSA, in accordance with various embodiments;
  • FIG. 31 shows thermograms of HSA, HWP and HDP with no added ligand, in accordance with various embodiments
  • FIG. 32 shows thermograms of HSA in the presence of increasing concentrations of ligand, in accordance with various embodiments
  • Figure 33 shows thermograms of HWP in the presence of increasing concentrations of ligand, in accordance with various embodiments
  • Figure 34 shows thermograms of HDP in the presence of increasing concentrations of ligand, in accordance with various embodiments
  • Figure 35 shows DSC thermograms of complement C3 at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ , in accordance with various embodiments;
  • Figure 36 shows DSC thermograms of complement C4 at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ , in accordance with various embodiments;
  • Figure 37 shows DSC thermograms of ceruloplasmin at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ , in accordance with various embodiments.
  • Figure 38 shows DSC thermograms of transferrin at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ , in accordance with various embodiments.
  • the description may use perspective-based descriptions such as up/down, back/front, and top/bottom. Such descriptions are merely used to facilitate the discussion and are not intended to restrict the application of disclosed embodiments.
  • Coupled and “connected,” along with their derivatives, may be used.
  • connection may be used to indicate that two or more elements are in direct physical or electrical contact with each other.
  • Coupled may mean that two or more elements are in direct physical or electrical contact. However, “coupled” may also mean that two or more elements are not in direct contact with each other, but yet still cooperate or interact with each other.
  • a phrase in the form "A/B” or in the form “A and/or B” means (A), (B), or (A and B).
  • a phrase in the form "at least one of A, B, and C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C).
  • a phrase in the form "(A)B” means (B) or (AB) that is, A is an optional element.
  • a computing system may be endowed with one or more components of the disclosed apparatuses and/or systems, and may be employed to perform one or more methods as disclosed herein.
  • DSC differential scanning calorimetry
  • the disclosed methods enable high-throughput methods for determining binding properties of an active ligand in a complex mixture of specific and non-specific protein targets.
  • Various embodiments of the systems and methods provided herein are based upon the observation that the extent to which a drug binds non-specifically to one or more plasma or serum proteins is determinative of the drug's in vivo distribution, availability for target binding, and rate of elimination.
  • a drug candidate compound in order for a drug candidate compound to be considered effective in a whole organism, it should be capable of being delivered effectively to a target tissue, target cell or target molecular receptor.
  • a drug candidate In general, to reach a tissue, a drug candidate must travel through the bloodstream to the desired target.
  • binding interactions of a drug candidate with non-target biomolecules e.g., proteins, lipids, nucleic acids, etc
  • a drug that is capable of binding to human serum albumin (HSA) may reach a drug target after traveling in the bloodstream.
  • HSA human serum albumin
  • the unbound drug concentration is more closely related to the activity of a drug than total plasma/serum concentration because generally only an unbound drug may pass through most cell membranes.
  • non-specific protein binding is also a factor in the determination of in vivo hepatic clearance based on in vitro intrinsic clearance.
  • Knowledge of the partitioning behavior of a therapeutic compound into red blood cells may be important to the interpretation and understanding of the compound's pharmacokinetic profile. For example, a high partitioning ratio may indicate accumulation of the compound in red blood cells, and potential
  • a drug compound that is capable of binding to a specific target molecule effectively in vitro may show substantially less efficacy than contemplated when administered in vivo.
  • the extent of binding to plasma proteins may be an important determinant of drug distribution and elimination (e.g., the D and E in ADMET).
  • DSC is a thermoanalytical technique in which the difference in the amount of heat required to increase the temperature of a sample and reference is measured as a function of temperature.
  • the basic principle is that when a sample undergoes a physical transformation such as a melting transition, more or less heat will flow to the sample than the reference to maintain both at the same temperature.
  • DSC measures the amount of heat absorbed or released during temperature induced transitions. This heat is essentially the enthalpy of the transition and, in various embodiments, may provide insights into the thermodynamic stability of the protein sample.
  • a ligand binds to a protein, it may affect stability of the protein structure and thereby induce changes in the shape of the heat signature, or thermogram.
  • the methods and systems disclosed herein may provide robust and reproducible results based on thermal stability rather than molecular weight and charge, which form the basis for conventional techniques (e.g., gel electrophoresis and mass spectrometry).
  • the disclosed embodiments also may require only very small samples and short processing times.
  • DSC-based methods and systems disclosed herein may provide unique insights into the origin of binding (e.g., hydrophobic vs. ionic interactions, or binding vs. non-binding). For example, in various embodiments, DSC titration of HSA with a ligand of known affinity shows clear binding characteristics, which may be illustrated by clear shifts in a DSC thermogram.
  • a fluorescence melting curve may be used as an independent measure of the shift in melting temperature, AT m , that occurs due to binding.
  • isothermal titration calorimetry ITC
  • ITC measurements also may provide an evaluation of the binding constant and the number of binding sites.
  • the following strategy may be used: if DSC provides AT m and altered ⁇ in the presence of the binding entity, further characterization may be achieved through fluoresence shift and ITC measurements.
  • systems and methods including integrated DSC-based systems and methods, that may be used to determine one or more favorable characteristics of in vivo absorption, distribution, metabolism, and/or excretion (ADME) for a biomolecule or small molecule ligand.
  • ADME in vivo absorption, distribution, metabolism, and/or excretion
  • collectively, the four ADME criteria may influence the drug levels and kinetics of drug exposure to tissues, and consequently, may influence the performance and pharmacological activity of the ligand.
  • data generated by the disclosed systems and methods may be utilized in conjunction with currently available in silico drug design software.
  • a differential scanning calorimeter may be integrated with a computer employing enthalpy of transition calculation software to produce a high-throughput system for measuring ADMET parameters to facilitate the early stage selection of drug candidates in a drug discovery process based upon the pharmacokinetic and pharmacodynamic profiles for a drug interaction with a biomolecule (such as, e.g., human serum albumin (HSA) or blood plasma).
  • a biomolecule such as, e.g., human serum albumin (HSA) or blood plasma.
  • the systems and methods described herein may be carried out under computer control, and may be used for selecting a lead drug candidate from a plurality of potential drug candidates.
  • lead drug candidates that are identified using these systems and methods may have reduced blood protein binding characteristics as compared to other candidate compounds.
  • the disclosed systems and methods may provide a means for identifying two or more candidate compounds from a selection of compounds that are capable of binding to a specific target molecule and, thereby, altering the function of the target molecule through in vivo activation, deactivation, catalysis, or inhibition.
  • the disclosed methods may include the step of combining two or more compounds from the plurality of compounds with at least one blood protein to form a first combination, a second combination, or more
  • Some embodiments may employ a device, such as a DSC, for generating and storing a range of physical transformation data, such as thermodynamic parameters, for one or more combinations of compounds and/or biomolecules, in operative combination with a computer that employs enthalpy of transition calculation software, for comparing and ranking physical transformation data for a first combination of compounds and/or biomolecules with physical transformation data for a second combination of compounds and/or biomolecules to permit the determination of one or more ADMET parameters for each of the first and second combinations.
  • a device such as a DSC
  • a device for generating and storing a range of physical transformation data, such as thermodynamic parameters, for one or more combinations of compounds and/or biomolecules, in operative combination with a computer that employs enthalpy of transition calculation software, for comparing and ranking physical transformation data for a first combination of compounds and/or biomolecules with physical transformation data for a second combination of compounds and/or biomolecules to permit the determination of one or more ADMET parameters for each of the first
  • the disclosed systems and methods may permit the identification of compounds that are less likely to bind to a protein, such as a plasma protein, as compared to other compounds that are more likely to bind to the protein, thus reducing their bioavailability.
  • a protein such as a plasma protein
  • ADME characteristics include: (a) measuring a first range of physical transformation data in a first combination having one or more biomolecule(s) without a drug candidate; (b) measuring a second range of physical transformation data in a second combination with the biomolecule(s) in the presence of the drug candidate; (c) calculating a difference between an enthalpy of transition for the first combination and the second combination ( ⁇ ) to obtain at least one binding interaction parameter for the binding of the drug candidate with the biomolecule(s); and (d) comparing the binding interaction parameter to at least one mathematical expression correlating binding interaction data measured for a known drug compound having a known pharmacokinetic parameter to determine at least one pharmacokinetic parameter of the drug candidate.
  • Some embodiments further include measuring a third range of physical transformation data in a third combination of the biomolecule(s) and a second drug candidate.
  • these methods employ a system, as described in further detail herein, that includes a DSC in operative combination with a computer that employs enthalpy of transition calculation software to permit the determination of one or more ADMET parameters.
  • the pharmacokinetic parameter may be an absorption parameter, a distribution parameter, a metabolism parameter, or an excretion parameter.
  • the pharmacokinetic parameter may be a volume of distribution, a total clearance, protein binding, tissue binding, metabolic clearance, renal clearance, hepatic clearance, biliary clearance, intestinal absorption, bioavailability, relative bioavailability, intrinsic clearance, mean residence time, maximum rate of metabolism, a Michaelis-Menten constant, a partitioning coefficient between a tissue and blood or plasma, a fraction excreted unchanged in urine, a fraction of drug systemically converted to a metabolite, an elimination rate constant, a half-life, or a secretion clearance.
  • partitioning coefficients between tissues and blood or plasma may be partitioning coefficients associated with the blood-brain barrier, blood-placenta barrier, blood-human milk partitioning, blood-adipose tissue partitioning, or blood-muscle partitioning.
  • the methods may further include: (a) estimating at least two pharmacokinetic parameters of a drug candidate and/or (b) estimating a solubility property of the drug candidate.
  • the mathematical expression correlated from binding interaction data associated with known drug compounds may be a function fitted to a plurality of data points plotted on a Cartesian coordinate system.
  • the method may include one or more biomolecules that are selected from isolated plasma proteins, liposomes, CYP 450 enzymes, metabolic enzymes, and transport proteins.
  • thermodynamic parameters for the binding of an active agent in a complex mixture of specific and non-specific molecules may be employed in methods to determine the varying degrees of T m shift and enthalpy changes for a particular binding site or active agent type.
  • the systems and methods disclosed herein may be utilized in vitro to decrease the failure rate of lead compounds due to adverse ADMET properties.
  • Any DSC may be used to carry out the disclosed methods, for instance a GE
  • MicroCal DSC MicroCal DSC, a TA Instruments DSC, a Perkin Elmer DSC, or the like.
  • the computing system may use software that may enable database creation and database comparison tools, and substitutions of software that is functionally equivalent is considered to be within the spirit and scope of the disclosure. DSC is discussed at greater length below.
  • DSC is a thermoanalytical technique that may be used to determine the difference in the amount of heat required to increase the temperature of a sample and a reference, measured as a function of temperature, and is described in U.S. Patent No.
  • the technique may include simultaneously applying heat to a sample material and a reference material.
  • a sample material goes through various physical and chemical changes such as crystallization, melting, freezing, oxidation, etc.
  • its temperature may be affected by the changes in internal energy.
  • the differences in temperature between the sample and reference may be recorded, and calculations may then be made for
  • determining the internal energy changes occurring in the sample when the sample undergoes a physical transformation such as a phase transition, more or less heat may need to flow to it than the reference in order to maintain both at the same temperature. Whether less or more heat must flow to the sample may depend on whether the process is exothermic or endothermic. For example, as a solid sample melts to a liquid, it may require more heat flowing to the sample to increase its temperature at the same rate as the reference. This is due to the absorption of heat by the sample as it undergoes the endothermic phase transition from solid to liquid. Likewise, as the sample undergoes an exothermic process (such as crystallization), less heat may be required to raise the sample temperature.
  • an exothermic process such as crystallization
  • differential scanning calorimeters may measure the amount of heat absorbed or released during such transitions.
  • DSC may also be used to observe subtler phase changes, such as glass transitions.
  • both the sample and the reference may be maintained at nearly the same temperature throughout the procedure.
  • the temperature program for a DSC analysis may be designed such that the sample holder temperature increases linearly as a function of time.
  • the reference sample may typically have a well-defined heat capacity over the range of temperatures to be scanned.
  • DSC may result in a curve of heat flux versus temperature or versus time.
  • this curve may be used to calculate enthalpies of transitions.
  • integrating the peak corresponding to a given transition may complete such calculations.
  • the calorimetric constant may vary from instrument to instrument, and may be determined by analyzing a well-characterized sample with known enthalpies of transition.
  • DSC machines or functional equivalents of DSC machines that may be used in the disclosed embodiments, such as GE MicroCal (22 Industrial Drive East, Northampton, MA 01060); TA Instruments (159 Lukens Drive, New Castle, DE 19720); and Perkin Elmer (710 Bridgeport Avenue, Shelton, CT 06484), among others.
  • alloys may have enhanced properties when compared to the pure substance (e.g., steel is stronger than iron).
  • mixtures do not necessarily have a single melting point, but may have a melting range in which the composition is a blend of solid and liquid phases. This combination, in turn, may produce a unique melting point.
  • a DSC plasma or serum thermogram may represent a composite melting curve of 3,000 or more proteins that make up the plasma proteome. Of these, only about 16 major blood proteins are present in a concentration sufficient for their melting curves to directly manifest in the DSC thermogram, in accordance with various embodiments.
  • the complicated plasma or serum mixture may be sensitive to interactions with a drug candidate.
  • a drug candidate may bind to or interact with one or more of the 16 major plasma proteins, which may alter one or more of the primary, secondary, tertiary, and/or quaternary structures.
  • the result may be a radical shift, in a mass weighted manner, in the DSC plasma thermogram of a sample containing a drug candidate that binds to a plasma protein, vs. a drug candidate that does not bind to a plasma protein.
  • DSC plasma thermograms may be sensitive to interactions between a drug candidate and any of the (approximately) 16 major plasma component proteins, some of which include HSA, transferrin, fibrinogen, and IGg.
  • HSA is the most abundant plasma protein
  • DSC may be used on a HSA sample to determine whether a drug candidate binds to HSA. Likewise, in various embodiments, DSC may be used to detect binding of a drug candidate to transferrin, fibrinogen, iGg, or any other plasma protein.
  • melting curves of plasma from human blood or a component thereof measured by DSC thermograms may be used to detect binding to a drug candidate.
  • a general statistical methodology was developed to analyze DSC plasma thermogram data sets collected for human plasma and components thereof.
  • the statistical metric may provide estimates of the likelihood that a particular drug candidate binds to plasma or a component thereof, such as HSA.
  • analysis of an acquired DSC thernnogrann may involve comparison to a database of empirical reference sets of DSC thermograms.
  • two parameters, a distance metric (P) and correlation coefficient (r) may be used to produce a combined 'similarity metric', p, which can be used to classify drug candidates regarding their ability to bind plasma proteins.
  • data may be collected over temperatures ranging from about 25 to 1 10 °C, with 10 measurements for each degree (C), resulting in p ⁇ 750.
  • a reference set of thermograms may then include a collection of N vectors ⁇ Xj(T)
  • j 1 ...N ⁇ , where each Xj(T) represents a single reference thermogram.
  • the median thermogram, x ref (T), of a reference set may be computed from all curves within the set and serves as a template thermogram representing that category.
  • variance within a reference set may be quantified at each temperature by upper and lower quantile vectors, a upP er(T) and ai OW er(T), respectively. These vectors establish the 0.05 to 0.95 quantile boundaries, wherein 90% of the measured data lies. In the case that a more detailed knowledge of the distributions underlying the data is obtained, these parameters may be refined using distribution-dependent measures of central tendency and variance.
  • the quality of a potential reference set of thermograms for a candidate drug may be assessed using descriptive statistical measures. For example, to determine the extent to which a given curve in the reference set aligns with the median thermogram, each reference curve may be compared to the median thermogram, and the linear correlation coefficient (rvalue) may be determined, resulting in a distribution of rvalues. In various embodiments, high levels (>0.8) of correlation may indicate that a given reference set is homogeneous in shape, and may be taken as an indication that the median thermogram x ref (T) may be reliably used as a template curve.
  • T median thermogram x ref
  • the Pearson's correlation coefficient may be used here, however, more general and non-parametric correlations (such as Kendall's tau) may also be used when appropriate.
  • quantile box-plots may also be constructed to assess the variability or degree of homogeneity in a reference set.
  • thermograms may be used, in accordance with the present disclosure. In general, these methods may be aimed primarily at addressing the diagnostic need; e.g., determining to what degree a test curve, xtest(T), aligns with a given reference template curve, x re f(T).
  • the degree of similarity between a test curve and a reference thermogram may be characterized by two factors: (1 ) closeness in space (standardized Euclidean distance) at each temperature point, and (2) similarity in shape (correlation).
  • two thermograms may be highly correlated but, due to vertical scaling, may be separated by a nontrivial distance in space.
  • two thermograms may be spatially close but poorly correlated due to fluctuations or noise in the data.
  • the metric employed to quantify similarities between test and reference curves may be a combination of both spatial distance and linear correlation.
  • distance between two curves may be any distance between two curves.
  • the standardized distance d(T) may be
  • d(T) > 1 may indicate that, at the temperature T, the test curve is more distant from the median than 90% of the data in the reference set.
  • d(T) may be interpreted as a z-score, and the probability distribution function representing the reference data may be used to compute critical values at each temperature.
  • the function p(T) may return high values (0.9) at temperatures for which the test curve falls within the quantile boundary, and may return low values (0.1 ) at temperatures for which the test curve falls outside the quantile boundary.
  • the function p(T) may represent a likelihood, based on quantile values, that the test curve is similar to the reference set at each temperature point. In various embodiments, no assumptions may be made about the distribution of the reference thermograms. As a result, in some embodiments, this choice of function may not be optimal for discrimination of test and template
  • thermograms In the case of a known distribution of reference curves, more
  • the metric P may be interpreted as a probability determined by the standardized multivariate distance between the test curve and the reference template.
  • a value of P near unity may indicate the test curve is closer to the reference template than 90% of the reference data, while a value of P near zero indicates that the test curve is more distant than 90% of the data.
  • similarity in shape between a test curve and a reference set may be quantified using a linear correlation, r, such as Pearson's or Kendall's tau correlation.
  • r such as Pearson's or Kendall's tau correlation.
  • two thermograms that are linearly correlated may necessarily possess similar shapes, so the linear correlation may be an effective measure for discriminating between curves different shapes (assuming similar scaling of the data).
  • linear correlation may provide valuable information about the shape of test curves, and may help to support and strengthen conclusions about degrees of similarity between test and reference curves.
  • any two thermograms may be highly correlated in certain temperature regions.
  • thermogram shape in very low (20-50°C) and very high (90-120°C) temperature regions, major differences in thermogram shape are seldom found.
  • the value of a linear correlation coefficient, r, between a test curve and a reference median curve may, in practice, never be negative, and may seldom even be close to zero.
  • interpretation of r-values with regard to the strength of relationship on an absolute scale may be done with some amount of care.
  • initial characterization of the data may help to determine significant levels of rfor interpretive use.
  • the relative scale of r may be more valuable, and may be established with training data and empirical calibration.
  • the range of p may be [0, 1 ], with values closer to zero indicating large differences in shape, and values approaching 1 indicating high similarity.
  • high values of both P and r in order to produce a high value of p, high values of both P and r may be necessary, while a small value of either P or r alone may be sufficient to produce a low p value.
  • the absolute scale for p may depend on the particular reference set (or sets) employed.
  • a relative scale may be empirically determined based on the training data, and the metric may be calibrated before application to unknown test curves.
  • the similarity metric, p may incorporate both distance and correlation into a single quantitative statistic that may then be used for discrimination between test curves and reference templates.
  • ITC may be used to confirm or refine a parameter detected by DSC.
  • ITC isothermal titration calorimetry
  • the disclosed system and methods may be used in various embodiments to detect binding of a drug candidate belonging to any class of drugs to a plasma or serum protein.
  • drug class refers to a classification method used to group a chemical or biological composition to the type of the active ingredient or by the way it is used to treat a particular condition, wherein each drug candidate may be capable of fitting into one or more drug classes.
  • drug candidate denotes one or more known or unknown chemical or biological compositions that may be classified by the chemical type of the active ingredient or by the way the drug candidate composition is proposed to treat a particular condition, wherein each "drug candidate composition” may be classified into one or more drug groups that having one or more of the following drug categories or drug classes including, but not limited to: ace inhibitors, addiction aids, aldosterone antagonists, alpha blockers, ALS agents, Alzheimer's disease drugs, aminoglycosides, anesthetics/sedatives, angiotensin II inhibitors, antacids, anti-arrhythmics, antibiotics, anti-cholinergics, anti-convulsants, anti- depressants, anti-diarrheals, anti-emetics, anti-fungals, anti-hepatitis agents, anti-herpetic agents, antihistamines, anti-hypertensive combinations, anti-hypertensives, anti-influenza agents, anti-
  • antitussives/expectorants benzodiazepines, beta blockers, bile acid sequestrants,
  • bisphosphonates burn preparations, calcium channel blockers, calcium supplements, cephalosporins, colony stimulating factors, corticosteroids, decongestants, antidiabetic agents, direct thrombin inhibitors, disease modifying agents, diuretics, erectile dysfunction drugs, fever inducing agents, fibrates, fluoroquinolones, H2 blockers, hypertension drugs, anti-HIV agents, hormone replacement drugs, interferons, immunizations, insulin, laxatives, low molecular weight heparins, macrolides, magnesium supplements, mouth & lip preparations, multiple sclerosis drugs, muscle relaxants, nasal preparations, neuromuscular blocking agents, nitrates, NSAIDs, ophthalmic preparations, otic preparations, opiates, anti-Parkinson's agents, analgesics, penicillins, phosphate supplements, potassium supplements, proton pump inhibitors, respiratory medications, statins, stimulants, tetracyclines, thrombolytics, thyroid
  • a goal of a drug discovery process is to identify and characterize a chemical compound or ligand, e.g., binder or biomolecule (e.g., receptor) that affects the function of one or more other biomolecules (e.g., a drug "target") in an organism, usually a biopolymer, via a potential molecular interaction or combination.
  • a chemical compound or ligand e.g., binder or biomolecule (e.g., receptor) that affects the function of one or more other biomolecules (e.g., a drug "target") in an organism, usually a biopolymer, via a potential molecular interaction or combination.
  • biopolymer refers to a macromolecule that comprises one or more of a protein, nucleic acid (DNA or RNA), peptide or nucleotide sequence or any portions or fragments thereof.
  • the term "biomolecule” refers to a chemical entity that comprises one or more of a biopolymer, carbohydrate, hormone, or other molecule or chemical compound, either inorganic or organic, including, but not limited to, synthetic, medicinal, drug-like, or natural compounds, or any portions or fragments thereof.
  • the target molecule may be a disease-related target protein or nucleic acid for which it is desired to affect a change in function, structure, and/or chemical activity in order to aid in the treatment of a patient disease or other disorder.
  • the target may be a biomolecule found in a disease- causing organism, such as a virus, bacteria, or parasite, that when affected by the drug will affect the survival or activity of the infectious organism.
  • the target may be a biomolecule of a defective or harmful cell such as a cancer cell.
  • the target may be an antigen or other environmental chemical agent that may induce an allergic reaction or other undesired immunological or biological response.
  • the ligand may be a small molecule drug or chemical compound with desired drug-like properties in terms of potency, low toxicity, membrane permeability, solubility, chemical/metabolic stability, etc.
  • the ligand may be biologic, such as a protein-based or peptide-based drug.
  • the ligand may be a chemical substrate of a target enzyme.
  • the ligand may even be covalently bound to the target, or may be a portion of the protein, e.g., protein secondary structure component, protein domain containing or near an active site, protein sub- unit of an appropriate protein quaternary structure, etc.
  • a (potential) molecular combination will feature at least one ligand and at least one target, the ligand and target are usually separate chemical entities, and the ligand will be assumed to be a chemical or biological compound while the target is typically a biological protein (mutant or wild type).
  • the term "molecular complex” refers to a bound state between target and ligand when interacting with one another in the midst of a suitable (often aqueous) environment.
  • a “potential" molecular complex refers to a bound state that may occur albeit with low probability and therefore may or may not actually form under normal conditions.
  • association means constituents are bound to one another either covalently or non-covalently, the latter as a result of hydrogen bonding or other intermolecular forces.
  • the constituents may be present in ionic, non-ionic, hydrated or other forms.
  • ADME characteristics that may be assessed by the methods disclosed in various embodiments include absorption/administration, distribution, metabolism, and
  • Absorption/administration refers to the process by which a compound reaches a target tissue following administration. This absorption often occurs on mucous surfaces like the digestive tract (intestinal absorption) - after being taken up by the target cells. This can be a serious problem at some natural barriers like the blood-brain barrier. Factors such as poor compound solubility, gastric emptying time, intestinal transit time, chemical instability in the stomach, and inability to permeate the intestinal wall can all reduce the extent to which a drug is absorbed after oral administration. Absorption critically determines the compound's bioavailability. Drugs that absorb poorly when taken orally must be administered in some less desirable way, like intravenously or by inhalation (e.g., zanamivir).
  • a compound For distribution, a compound must be carried to its effective site, most often via the bloodstream. From there, the compound may distribute into tissues and organs, usually to differing extents. After entry into the systemic circulation, either by intravascular injection or by absorption from any of the various extracellular sites, the drug is subjected to numerous distribution processes that tend to lower its plasma concentration. Distribution is defined as the reversible transfer of a drug between one compartment to another. Some factors affecting drug distribution include regional blood flow rates, molecular size, polarity and binding to serum proteins, forming a complex.
  • a compound such as a small-molecule drug
  • an active form typically in the liver by redox enzymes known as cytochrome P450 enzymes.
  • the initial (parent) compound is converted to new compounds called metabolites.
  • metabolites are pharmacologically inert, metabolism deactivates the administered dose of parent drug and this usually reduces the effects on the body. Metabolites may also be pharmacologically active, sometimes more so than the parent drug.
  • Excretion of drugs by the kidney involves 3 main mechanisms: glomerular filtration of unbound drug; active secretion of (free & protein-bound) drug by transporters e.g., anions such as urate, penicillin, glucuronide, sulfate conjugates or cations such as choline, histamine; and filtrate, wherein a 100-fold concentrated substance in tubules form a favorable concentration gradient so that it may be reabsorbed by passive diffusion and passed out through the urine.
  • transporters e.g., anions such as urate, penicillin, glucuronide, sulfate conjugates or cations such as choline, histamine
  • filtrate wherein a 100-fold concentrated substance in tubules form a favorable concentration gradient so that it may be reabsorbed by passive diffusion and passed out through the urine.
  • binding mode refers to the 3-D molecular structure of a potential molecular complex in a bound state at or near a minimum of the binding energy (e.g., maximum of the binding affinity), where the term “binding energy” (sometimes interchanged with “binding free energy” or with its conceptually antipodal counterpart “binding affinity”) refers to the change in free energy of a molecular system upon formation of a potential molecular complex, e.g., the transition from an unbound to a (potential) bound state for the ligand and target.
  • binding energy sometimes interchanged with “binding free energy” or with its conceptually antipodal counterpart "binding affinity” refers to the change in free energy of a molecular system upon formation of a potential molecular complex, e.g., the transition from an unbound to a (potential) bound state for the ligand and target.
  • system pose is also sometimes used to refer to the binding mode.
  • free energy generally refers to both enthalpic and entropic effects as the result of physical interactions between the constituent atoms and bonds of the molecules between themselves (e.g., both intermolecular and intramolecular interactions) and with their surrounding environment, meaning the physical and chemical surroundings of the site of reaction between one or more molecules.
  • free energy are the Gibbs free energy encountered in the canonical or grand canonical ensembles of equilibrium statistical mechanics.
  • the optimal binding free energy of a given target-ligand pair directly correlates to the likelihood of combination or formation of a potential molecular complex between the two molecules in chemical equilibrium.
  • the binding free energy describes an ensemble of (putative) complex structures and not one single binding mode.
  • the change in free energy is dominated by a single structure corresponding to a minimal energy. This is generally true for tight binders (pK ⁇ 0.1 to 10 nanomolar) but questionable for weak ones (pK ⁇ 10 to 100 micromolar).
  • the dominating structure is usually taken to be the binding mode. In some cases, it may be necessary to consider more than one alternative-binding mode when the associated system states are nearly degenerate in terms of energy.
  • Binding affinity is of direct interest to drug discovery and rational drug design because the interaction of two molecules, such as a protein that is part of a biological process or pathway and a drug candidate sought for targeting a modification of the biological process or pathway, often helps indicate how well the drug candidate will serve its purpose. Furthermore, where the binding mode is determinable, the action of the drug on the target can be better understood. Such understanding may be useful when, for example, it is desirable to further modify one or more characteristics of the ligand to improve its potency (with respect to the target), binding specificity (with respect to other target biopolymers), or other chemical and metabolic properties. In various embodiments, the interaction of two known molecules may be significantly altered with the introduction of one or more unknown molecules into the two- molecule system. Various embodiments may allow a user to analyze one or more drug candidates together with one or more biological targets, and the resulting data may be useful for further drug modification or new drug selection.
  • thermograms may show changes in the "forest rather than in a single tree," or the single tree depending on the procedure.
  • EXAMPLE 1 NEW DRUG DEVELOPMENT - ADME SCREENING
  • Example 1 illustrates the use of DSC as a screening tool for determining parameters for absorption, distribution, metabolism, elimination (ADME) or toxicity of candidate drug compositions.
  • Figures 1A and 1 B illustrate the results of DSC and fluorescence melting curves of HSA at a concentration of 25 ⁇ in phosphate buffer (left) and in the same phosphate buffer plus 5% DMSO (right). The DSC melting curve is shown on the far left of both panels. The DSC curve had a single peak with low temperature and high temperature linear baselines preceding and succeeding the melting transition. The integrated area under the baseline corrected DSC melting curve provides a measure of the melting transition enthalpy, ⁇ .
  • DMSO is used as an additive to improve solubility of added binding ligands.
  • the observation that there were no differences in the melting curve parameters with and without DMSO indicates that the effect of DMSO is confined to its intended purpose of only enhancing ligand solubility, while not affecting the stability of HSA.
  • Figure 2 shows an example of measurements of ligand binding to HSA using three different technologies: (1 ) DSC, (2) fluorescence melting, and (3) ITC. On the right side of Figure 2 is the DSC melting curve (top) of the ligand bromocresol green that binds to HSA. The ratio of HSA to ligand concentration was 1 :10.
  • thermodynamic parameters determined by DSC were increased when compared to the values in Figures 1A and 1 B. More specifically, in the presence of the ligand, ⁇ increased from 1000.52 to 1021.25 kJ/mol.
  • the DSC T max increased by 9.26° C and the fluorescent melting temperature, T m , increased by 10.7°C.
  • T m fluorescent melting temperature
  • ITC experiments may be performed.
  • the ligand is added at a constant temperature in a titrating fashion to the HSA, and the enthalpy and entropy of binding are determined as a function of relative concentration of ligand to HSA.
  • the heat response (enthalpy) measured by ITC upon addition of the ligand at increasing molar ratios of the ligand to HSA is shown for a series of injections in the top of Figure 2 on the left.
  • a comparison of the values obtained using DSC and the other two methods indicates that all three methods provide consistent results, and indicate that at least two sites on HSA are bound by the ligand, in this case, bromocresol green.
  • ITC may provide quantitative insight into the type of binding that occurs, it is not easily amenable to high throughput.
  • the screening strategy if the DSC and/or fluorescence melting curves reveal a shift due to the ligand, then ITC may be enlisted to more deeply characterize the binding mechanism where required.
  • EXAMPLE 2 BINDING OF HSA BY NAPROXEN
  • Naproxen sodium is a nonsteroidal anti-inflammatory drug (NSAID) commonly used for the reduction of pain, fever, and inflammation.
  • NSAID nonsteroidal anti-inflammatory drug
  • the diagrams shown in Figure 3 are similar to those found in Figure 2, with the exception that HSA is bound by naproxen.
  • ITC binding curves are shown on the left of Figure 3, along with fitted parameters obtained for the two-site binding model, derived from the two software routines described above.
  • the DSC and fluorescence melting curves are shown on the right, along with the melting curve parameters that were measured.
  • the increased ⁇ , T max , and T m values of HSA in the presence of the ligand verify the ligand binds HSA.
  • the results of two independent ITC measurements and fits of the saturated binding curves indicate the binding mechanism is complicated, can be fit with the two site model, but from the differences in their ITC binding curves, binding behavior of naproxen to HSA is clearly different than binding of bromocresol green in Figure 2.
  • EXAMPLE 3 BINDING OF HSA BY SALICYLA TE
  • FIGs shown in Figure 4 are similar to those found in Figure 3, with the exception that HSA is bound by salicylate.
  • Replicate ITC binding curves are shown on the left of Figure 4, along with fitted parameters obtained using a single site model (as opposed to the multi-site models that were fitted in Figures 2 and 3. (Generally, the two-site model is invoked if the data cannot be fit satisfactorily with a one site model).
  • the much smaller values of ⁇ , T max , and T m for HSA in the presence of the ligand verify that the ligand binds HSA, but in a much weaker fashion than do the ligands shown in Figures 2 and 3.
  • the simple linear binding curves were measured by ITC.
  • the observation of relatively weak binding to HSA by salicylate indicates that it has properties desired for a better drug candidate compared to those of naproxen ( Figure 3) and bromocresol green ( Figure 2), which bind HSA much more strongly.
  • EXAMPLE 4 BINDING OF HSA BY BROMOPHENOL BLUE
  • Bromophenol blue is commonly used as a color marker to monitor the process of agarose gel electrophoresis and polyacrylamide gel electrophoresis.
  • bromophenol blue is not considered a drug, it may be used as an example of a drug candidate that carries a slight negative charge at moderate pH, wherein it can bind with proteins and give blue color.
  • the diagrams shown in Figure 5 are similar to those found in Figure 2, with the exception that HSA is bound by bromophenol blue.
  • the binding of HSA by bromophenol blue is similar to the binding of bromocresol green in Figure 2.
  • the increased values of ⁇ , T max and T m of HSA in the presence of the ligand verify that the ligand binds HSA.
  • EXAMPLE 5 BINDING OF HSA BY PHENOL RED
  • the binding curve in Figure 6B is the "excess site binding" curve as was measured at molar ratios where the HSA is vast excess of ligand. These procedures were performed to precisely evaluate an enthalpy for the average binding of the ligand to HSA.
  • the enthalpy evaluated in the excess site binding case is -2,399.8 cal/mol, which is about 65% larger than that evaluated from fitting the saturation binding curve with the single site model. This indicates the deficiency of the single site model, and shows that there may be more than one site.
  • EXAMPLE 6 BINDING OF USA BY BROMOSULFALEIN
  • EXAMPLE 7 BINDING OF HSA BY IBUPROFEN
  • EXAMPLE 8 BINDING OF HSA BY IMIPRAMINE
  • EXAMPLE 10 BINDING OF HSA BY OXACILLIN
  • EXAMPLE 11 BINDING OF HSA BY PENICILLIN G
  • EXAMPLE 12 BINDING OF HSA BY EVAN'S BLUE
  • EXAMPLE 14 BINDING OF HSA BY OCTANOA TE
  • EXAMPLE 15 BINDING OF HSA BY SODIUM OLEATE
  • EXAMPLE 16 SUMMARY OF HSA BINDING BY 18 DIFFERENT LIGANDS
  • EXAMPLE 17 SUMMARY OF HSA BINDING BY 15 DIFFERENT LIGANDS
  • EXAMPLE 18 SUMMARY OF HSA BINDING GROUPED BY BINDING SITE
  • EXAMPLE 19 ITC SATURATION BINDING SUMMARY
  • EXAMPLE 20 ITC EXCESS SITE BINDING SUMMARY
  • EXAMPLE 21 ITC EXCESS SITE BINDING SUMMARY
  • FIGURE 22 shows a comparison of information provided by surface plasmon resonance (SPR) and DSC.
  • SPR provides indirect measurements of the free-energy, ⁇ G, for a binding reaction.
  • DSC provides values of the entropy, ⁇ , and entropy (AS), that together comprise AG and provide not only an evaluation of the binding strength, but in addition to the binding strength also yield insights into the precise
  • thermodynamic nature and origins of interactions driving the binding reaction For the binding reaction, SPR can only provide an indirect estimate of AG. DSC provides a direct measure of ⁇ , AS, and AG.
  • EXAMPLE 22 INTERACTION OF HSA WITH THREE AGENTS
  • FIG. 24 Ligands having relative binding strengths for HSA, relative shifts in the DSC melting profile are observed. The stronger the binding, the larger the shift.
  • EXAMPLE 24 DSC SCREEN FOR MAJOR PLASMA PROTEINS
  • EXAMPLE 25 DSC AND DRUG INTERACTIONS WITH WHOLE PLASMA
  • EXAMPLE 26 BINDING OF PENICILLIN AND BILIRUBIN TO HSA
  • thermograms were measured for HSA in the presence of increasing concentrations of each ligand. As shown in Figures 27 and 28, HSA thermograms were affected differently by the presence of penicillin and bilirubin. In Figure 27, for HSA at 10 ⁇ , as penicillin is increased from 0.1 to 50 ⁇ , the thermogram peak temperature (T m ) shifted up and increased in height with increasing ligand
  • thermogram 28 again for HSA at 10 ⁇ , as bilirubin was increased from 0.1 to 50 ⁇ , the behavior of the thermogram with increased ligand concentration differed from penicillin. As ligand concentration increased, the thermogram peak height increased, but the T m did not significantly change. In independent ITC experiments, bilirubin was shown to bind HSA in a pseudo multiple-site manner. Clearly, DSC can not only detect ligand binding, but can also distinguish between the different types of binding to HSA by penicillin and bilirubin. In order to obtain insight into the origins of these differences in binding, a simple binding model analysis was performed.
  • C p Tot C N + C NL + C D + C DL
  • the fraction of molecules in each state e.g., N L , N, D and D L
  • AH f N ⁇ AH NL + f D - AH ND + f DL - AH DL
  • EXAMPLE 27 DETECTION AND CHARACTERIZATION OF UNKNOWN
  • Figure 33 shows the effect of ligand binding to whole plasma.
  • EXAMPLE 28 SPECIFICITY STUDIES
  • Transferrin TRF
  • Ceruloplasmin Changes in the protein thermograms with increased concentrations of added compound revealed the presence of binding to the protein.
  • the individual protein was present at 10 ⁇ and the ligand was present at 0.1 , 1 .0 and 10 ⁇ , corresponding to molar ratios of protein to ligand of 100:1 , 10:1 , and 1 :1 , respectively.
  • Figure 35 shows DSC thermograms of complement C3 at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ . Compared to the C3 thermogram in the absence of ligand (solid line), at the lowest ligand concentration (0.1 ⁇ ) the thermogram actually decreased in intensity with no change in transition temperature. At the higher concentration in the presence of ligand, the thermogram intensity increased significantly, but again with no change in the transition temperature.
  • Figure 36 shows DSC thermograms of complement C4 at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ .
  • the thermogram peak increased in intensity with increased ligand concentration with little or no change in transition temperature.
  • the thermogram intensity decreased below that of the protein alone.
  • Figure 37 shows DSC thermograms of ceruloplasmin at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ . Compared to the ceruloplasmin thermogram in the absence of ligand (solid line), as ligand concentration increased, the thermogram peak increased with minor changes in the transition temperature. These observations further indicate reasonable binding of the ligand to ceruloplasmin.
  • Figure 38 shows DSC thermograms of transferrin at 10 ⁇ in the absence and presence of added ligand at 0.1 , 1 .0 and 10 ⁇ . Compared to the transferrin thermogram in the absence of ligand (solid line), as ligand concentration increased, the thermogram peak intensity decreased with no changes in the transition temperatures. In contrast to what was observed for complement C3, complement C4 and ceruloplasmin, these observations indicate relatively weaker TRF binding to the ligand.

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Abstract

L'invention concerne des systèmes et des procédés de détermination de profils pharmacocinétiques et pharmacodynamiques pour un médicament candidat potentiel, tel qu'une petite molécule ou un ligand. Dans divers modes de réalisation, les systèmes et procédés de l'invention peuvent combiner un dispositif, tel qu'un calorimètre à balayage différentiel (DSC), avec un ordinateur qui utilise un logiciel pour le calcul de paramètres thermodynamiques, tels que des enthalpies de transition pour une combinaison de molécules à l'intérieur d'un mélange. Divers modes de réalisation comprennent des procédés de détermination d'un médicament candidat ou d'une interaction de ligand avec une ou plusieurs biomolécules telles que, par exemple, l'albumine de sérum humain ou d'autres protéines du plasma sanguin.
PCT/US2012/024369 2011-02-08 2012-02-08 Procédé de détermination de caractéristiques de liaison d'un médicament candidat à une protéine WO2012109383A2 (fr)

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WO2014151310A3 (fr) * 2013-03-15 2014-11-13 Schrodinger, Llc Estimation de fermetures de cycles dans les affinités et les erreurs de liaison relative
WO2017066800A1 (fr) * 2015-10-15 2017-04-20 University Of Louisville Research Foundation, Inc Procédés de caractérisation et/ou de prédiction du risque associé à un échantillon biologique à l'aide de profils de stabilité thermique
CN111156922A (zh) * 2019-12-23 2020-05-15 苏州迭慧智能科技有限公司 一种利用外形轮廓测量的方法
CN112816637A (zh) * 2020-06-17 2021-05-18 湖南慧泽生物医药科技有限公司 一种吗替麦考酚酯片的体外溶出方法
JP2021177172A (ja) * 2020-05-08 2021-11-11 ネッチ ゲレーテバウ ゲーエムベーハー 生体物質を分析するための方法及びシステム、並びにそのようなシステムの使用
US11835529B1 (en) 2019-10-24 2023-12-05 University Of Louisville Research Foundation, Inc. Plasma thermograms for diagnosis and treatment of acute myocardial infarction

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EP1320749A2 (fr) * 2000-09-05 2003-06-25 The Althexis Company Recherche de medicaments par triage de cibles calorimetriques
CA2540582A1 (fr) * 2003-10-03 2005-04-21 Alza Corporation Procede de criblage pour l'evaluation de l'interaction bicouche-medicament dans des compositions liposomiques
US7371006B2 (en) * 2004-02-10 2008-05-13 Perkinelmer Las, Inc. Differential scanning calorimeter (DSC) with temperature controlled furnace
NZ578283A (en) * 2007-01-12 2012-09-28 Univ Louisville Res Found Proteomic profiling method useful for condition diagnosis and monitoring, composition screening, and therapeutic monitoring

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014151310A3 (fr) * 2013-03-15 2014-11-13 Schrodinger, Llc Estimation de fermetures de cycles dans les affinités et les erreurs de liaison relative
JP2016515273A (ja) * 2013-03-15 2016-05-26 シュレーディンガー エルエルシーＳｃｈｒｏｄｉｎｇｅｒ，Ｌｌｃ 相対結合親和性および誤差のサイクルクロージャ推定
WO2017066800A1 (fr) * 2015-10-15 2017-04-20 University Of Louisville Research Foundation, Inc Procédés de caractérisation et/ou de prédiction du risque associé à un échantillon biologique à l'aide de profils de stabilité thermique
US11835529B1 (en) 2019-10-24 2023-12-05 University Of Louisville Research Foundation, Inc. Plasma thermograms for diagnosis and treatment of acute myocardial infarction
CN111156922A (zh) * 2019-12-23 2020-05-15 苏州迭慧智能科技有限公司 一种利用外形轮廓测量的方法
JP2021177172A (ja) * 2020-05-08 2021-11-11 ネッチ ゲレーテバウ ゲーエムベーハー 生体物質を分析するための方法及びシステム、並びにそのようなシステムの使用
CN112816637A (zh) * 2020-06-17 2021-05-18 湖南慧泽生物医药科技有限公司 一种吗替麦考酚酯片的体外溶出方法

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